Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above. For any included plots, make sure they are clearly
labeled. You are free to use any plot type that you feel best
communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
library(tidyverse)
library(janitor)
library(here)
library(ggmap)
#library(albersusa)
#Albersusa is not loading for me. Not sure why.
We will use two separate data sets for this homework.
The first data set represent sightings of grizzly bears (Ursos arctos) in Alaska.
The second data set is from Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
Load the grizzly data and evaluate its
structure.
grizzy <- readr::read_csv("data/bear-sightings.csv")
## Rows: 494 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): bear.id, longitude, latitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
clean_names(grizzy)
## # A tibble: 494 × 3
## bear_id longitude latitude
## <dbl> <dbl> <dbl>
## 1 7 -149. 62.7
## 2 57 -153. 58.4
## 3 69 -145. 62.4
## 4 75 -153. 59.9
## 5 104 -143. 61.1
## 6 108 -150. 62.9
## 7 115 -152. 68.0
## 8 116 -147. 62.6
## 9 125 -157. 60.2
## 10 135 -156. 58.9
## # … with 484 more rows
summary(grizzy)
## bear.id longitude latitude
## Min. : 7 Min. :-166.2 Min. :55.02
## 1st Qu.:2569 1st Qu.:-154.2 1st Qu.:58.13
## Median :4822 Median :-151.0 Median :60.97
## Mean :4935 Mean :-149.1 Mean :61.41
## 3rd Qu.:7387 3rd Qu.:-145.6 3rd Qu.:64.13
## Max. :9996 Max. :-131.3 Max. :70.37
anyNA(grizzy)
## [1] FALSE
#Weird to have no NA's, NA might be listed as something else in the data
lat <- c(55.02, 70.37)
long <- c(-166.2, -131.3)
g_bbox <- make_bbox(long, lat, f = 0.05)
stamen in a terrain style projection
and display the map.mapbear <- get_map(g_bbox, maptype = "terrain", source = "stamen")
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
ggmap::ggmap(mapbear)
ggmap(mapbear) +
geom_point(data = grizzy, aes(longitude, latitude),size = 0.35, color = "Green") +
labs(x= "longitude", y= "latitude", title="Grizzly Bears in Alaska")
Let’s switch to the wolves data. Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
wolves <- readr::read_csv("data/wolves_data/wolves_dataset.csv")
## Rows: 1986 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): pop, age.cat, sex, color
## dbl (19): year, lat, long, habitat, human, pop.density, pack.size, standard....
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(wolves)
## Rows: 1,986
## Columns: 23
## $ pop <chr> "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "…
## $ year <dbl> 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 200…
## $ age.cat <chr> "S", "S", "A", "S", "A", "A", "A", "P", "S", "P", "…
## $ sex <chr> "F", "M", "F", "M", "M", "M", "F", "M", "F", "M", "…
## $ color <chr> "G", "G", "G", "B", "B", "G", "G", "G", "G", "G", "…
## $ lat <dbl> 57.03983, 57.03983, 57.03983, 57.03983, 57.03983, 5…
## $ long <dbl> -157.8427, -157.8427, -157.8427, -157.8427, -157.84…
## $ habitat <dbl> 254.08, 254.08, 254.08, 254.08, 254.08, 254.08, 254…
## $ human <dbl> 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10…
## $ pop.density <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, …
## $ pack.size <dbl> 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.7…
## $ standard.habitat <dbl> -1.6339, -1.6339, -1.6339, -1.6339, -1.6339, -1.633…
## $ standard.human <dbl> -0.9784, -0.9784, -0.9784, -0.9784, -0.9784, -0.978…
## $ standard.pop <dbl> -0.6827, -0.6827, -0.6827, -0.6827, -0.6827, -0.682…
## $ standard.packsize <dbl> 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3…
## $ standard.latitude <dbl> 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7…
## $ standard.longitude <dbl> -2.1441, -2.1441, -2.1441, -2.1441, -2.1441, -2.144…
## $ cav.binary <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ cdv.binary <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cpv.binary <dbl> 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ chv.binary <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ neo.binary <dbl> NA, NA, NA, 0, 0, NA, NA, 1, 0, 1, NA, 0, NA, NA, N…
## $ toxo.binary <dbl> NA, NA, NA, 1, 0, NA, NA, 1, 0, 0, NA, 0, NA, NA, N…
clean_names(wolves)
## # A tibble: 1,986 × 23
## pop year age_cat sex color lat long habitat human pop_dens…¹ pack_…²
## <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AK.PEN 2006 S F G 57.0 -158. 254. 10.4 8 8.78
## 2 AK.PEN 2006 S M G 57.0 -158. 254. 10.4 8 8.78
## 3 AK.PEN 2006 A F G 57.0 -158. 254. 10.4 8 8.78
## 4 AK.PEN 2006 S M B 57.0 -158. 254. 10.4 8 8.78
## 5 AK.PEN 2006 A M B 57.0 -158. 254. 10.4 8 8.78
## 6 AK.PEN 2006 A M G 57.0 -158. 254. 10.4 8 8.78
## 7 AK.PEN 2006 A F G 57.0 -158. 254. 10.4 8 8.78
## 8 AK.PEN 2006 P M G 57.0 -158. 254. 10.4 8 8.78
## 9 AK.PEN 2006 S F G 57.0 -158. 254. 10.4 8 8.78
## 10 AK.PEN 2006 P M G 57.0 -158. 254. 10.4 8 8.78
## # … with 1,976 more rows, 12 more variables: standard_habitat <dbl>,
## # standard_human <dbl>, standard_pop <dbl>, standard_packsize <dbl>,
## # standard_latitude <dbl>, standard_longitude <dbl>, cav_binary <dbl>,
## # cdv_binary <dbl>, cpv_binary <dbl>, chv_binary <dbl>, neo_binary <dbl>,
## # toxo_binary <dbl>, and abbreviated variable names ¹​pop_density, ²​pack_size
anyNA(wolves)
## [1] TRUE
#NA's present
d_wolves <- wolves %>%
filter(lat<50)
max(d_wolves$lat)
## [1] 47.74968
min(d_wolves$lat)
## [1] 33.88778
max(d_wolves$long)
## [1] -86.81887
min(d_wolves$long)
## [1] -110.9924
lat <- c(33.88778, 47.74968)
long <- c(-110.9924, -86.81887)
w_bbox <- make_bbox(long, lat, f = 0.05)
stamen in a terrain-lines
projection and display the map.mapwolves <- get_map(w_bbox, maptype = "terrain-lines", source = "stamen")
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
ggmap::ggmap(mapwolves)
ggmap::ggmap(mapwolves)+
geom_point(data=d_wolves,aes(x=long,y=lat))+
labs(title="Wolves in the 48 Lower States", x="Longitude", y="Latitude")
fill and color by population.ggmap::ggmap(mapwolves)+
geom_point(data=d_wolves,aes(x=long,y=lat,fill = pop, color = pop),size=3.8, alpha=0.65)+
labs(title="Wolves in the 48 Lower States", x="Longitude", y="Latitude")+
theme_classic()
Please be sure that you check the keep md file in the
knit preferences.